Pilot of New Technologies to Increase the Genomic Diagnosis of Undiagnosed Disease Network (UDN) Patients

新技术试点以提高未确诊疾病网络 (UDN) 患者的基因组诊断

• 批准号: 10377756
• 负责人: Carlos A. Bacino
• 金额: $ 14.99万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10377756
• 关键词: Administrative Supplement; Alleles; Award; Biological Assay; Complement; DNA; Data; Diagnosis; Diagnostic; Disease; Epigenetic Process; Exons; Genes; Genomics; Goals; Health; Hi-C; Investigation; Laboratories; Modeling; Molecular Diagnosis; Nucleotides; Parents; Patients; Phase; Protein Isoforms; Research; Sampling; Sequence Analysis; Structure; Technology; Transcript; Untranslated RNA; Variant; clinical research site; epigenomics; exome sequencing; genetic variant; genome sequencing; genomic data; improved; insertion/deletion mutation; new technology; parent grant; programs; sequencing platform; success; transcriptome; transcriptome sequencing; transcriptomics; whole genome

Abstract: Despite the diagnostic successes of the UDN program, a significant number of patients remain genetically undiagnosed after exome sequencing, genome sequencing, and transcriptome analyses. We hypothesize that longer-range data from complementary sequence analysis platforms will enhance diagnostic yield in the UDN by identifying structural variants with phasing that remain undetected using traditional short- read sequencing platforms. We propose that longer-range sequencing technologies, such as Hi-C Whole Genome Sequencing (WGS) and long-read RNAseq, will facilitate the allele-specific characterization of variants (single nucleotide, indel, and structural variants), allow transcript isoform analysis in addition to phasing of cis vs. trans variants, provide information about epigenetic and nucleomic features such as domains, loops and compartments, that will enable additional integrated analysis of all forms of noncoding variants to improve the diagnostic yield. To do this we will pilot two additional data types for UDN patient assessment: Hi-C WGS, and long-read RNAseq. This will complement established technologies in the UDN including short-read WGS and short-RNAseq, as well as add to our current investigations on long-read WGS.

摘要： 尽管普遍糖尿病肾病方案的诊断取得了成功，但仍有相当数量的患者 在外显子组测序、基因组测序和转录组分析后未被诊断为遗传学。我们 假设来自互补序列分析平台的较长范围的数据将增强诊断 通过识别具有阶段化的结构变体来实现UDN的收益，这些变体仍然没有使用传统的短消息 阅读测序平台。我们建议更远距离的测序技术，如Hi-C整体 基因组测序(WGS)和长读RNAseq，将促进等位基因特异性的表征 变异体(单核苷酸、内切变异体和结构变异体)除了允许转录异构体分析外，还可以进行 顺式和反式变异体的分期，提供关于表观遗传和核组特征的信息，例如 域、循环和隔间，这将允许对所有形式的非编码进行额外的集成分析 以提高诊断率。为此，我们将为UDN患者试用两种额外的数据类型 评估：HI-C WGS和长期阅读的RNAseq。这将是对UDN中已有技术的补充 包括短读WGS和短RNAseq，以及增加我们目前对长读WGS的研究。